Rekonseptualisasi Ilmu Pengetahuan dan Metode Ilmiah: Kritik Paradigma Klasik dan Tantangan Penelitian Modern

The development of digital technology, big data, and artificial intelligence has fundamentally changed the way scientific knowledge is produced, validated, and used. The classical scientific paradigm, which relies on objectivity, linearity of methods, and empirical verification, is no longer entirely adequate to explain contemporary phenomena that are complex, dynamic, and non-linear. In this context, this study highlights the urgency of reconceptualizing science and scientific methods in order to remain relevant to modern research challenges. This article aims to identify the limitations of the classical scientific paradigm in understanding modern reality, analyze the transformation of scientific methods as a result of developments in computational technology and data analytics, and formulate directions for reconceptualizing science and research methodologies in line with the needs of the digital age. The research uses a conceptual approach through the integration of theory, epistemological criticism, and empirical findings from previous studies The results of the study show that classical epistemology is insufficient to capture the adaptive and networked phenomena that arise from socio-technological interactions. Scientific methods are shifting towards non-linear, data-driven, and complexity-based approaches, where patterns can be identified before hypotheses are formulated. In addition, technologies such as machine learning and predictive computing are forming new epistemic structures that impact scientific validity. The implications of the study emphasize the need for multi-method mastery, digital and algorithmic literacy, and the integration of ethical principles as core components of modern scientific methodology.